Demo Data from SIGIR 2026 / reviewers in the wild / expert
Amar Dhokia
dblp:224/9450 · also Amar Pravin Dhokia
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 50% Image and video coding · 50% | |
| Human-computer interaction and pervasive computing
1 paper |
Haptics and multimodal interaction · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › virtual environment
multimodal virtual environment |
0.4 | 1 | 2019 | Audio-Visual-Olfactory Resource Allocation for Tri-modal Virtual Environments · IEEE Trans. Vis. Comput. Graph. 2019 |
Image and video coding › image quality assessment › perceptual image quality assessment
perceptual quality prediction |
0.4 | 1 | 2019 | Audio-Visual-Olfactory Resource Allocation for Tri-modal Virtual Environments · IEEE Trans. Vis. Comput. Graph. 2019 |
Haptics and multimodal interaction › olfactory interfaces
olfactory display |
0.1 | 1 | 2019 | Audio-Visual-Olfactory Resource Allocation for Tri-modal Virtual Environments · IEEE Trans. Vis. Comput. Graph. 2019 |
Methods — techniques the papers use, named apart from their topics
quality prediction model · 0.8psychophysical experiment · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Audio-Visual-Olfactory Resource Allocation for Tri-modal Virtual EnvironmentsabstractVirtual Environments (VEs) provide the opportunity to simulate a wide range of applications, from training to entertainment, in a safe and controlled manner. For applications which require realistic representations of real world environments, the VEs need to provide multiple, physically accurate sensory stimuli. However, simulating all the senses that comprise the human sensory system (HSS) is a task that requires significant computational resources. Since it is intractable to deliver all senses at the highest quality, we propose a resource distribution scheme in order to achieve an optimal perceptual experience within the given computational budgets. This paper investigates resource balancing for multi-modal scenarios composed of aural, visual and olfactory stimuli. Three experimental studies were conducted. The first experiment identified perceptual boundaries for olfactory computation. In the second experiment, participants ( N=25) were asked, across a fixed number of budgets ( M=5), to identify what they perceived to be the best visual, acoustic and olfactory stimulus quality for a given computational budget. Results demonstrate that participants tend to prioritize visual quality compared to other sensory stimuli. However, as the budget size is increased, users prefer a balanced distribution of resources with an increased preference for having smell impulses in the VE. Based on the collected data, a quality prediction model is proposed and its accuracy is validated against previously unused budgets and an untested scenario in a third and final experiment. Efstratios Doukakis, Kurt Debattista, Thomas Bashford-Rogers, Amar Dhokia, Ali Asadipour 0001, Alan Chalmers, Carlo Harvey |
IEEE Trans. Vis. Comput. Graph. | 4 |